Jasmine Patel

you@email.com · (669) 555-0103 · San Jose, United States linkedin.com/in/repolish-ai

Data Scientist (New Graduate)

Technical Skills

• Technical Skills: Python SQL PyTorch Spark Data Visualization
• Soft Skills: Communication Problem Solving Cross-functional Collaboration

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Summary

Recent M.S. graduate in Data Science with strong foundations in machine learning, statistical analysis, and data engineering. Published research in NLP and hands-on experience building predictive models at two tech companies. Passionate about applying data science to solve real-world problems.

Experience

Data Science Intern — Adobe

2024-06 – 2024-09

• Built a customer churn prediction model using gradient boosting and feature engineering, achieving 89% AUC and identifying key drivers of customer attrition.
• Developed automated data pipelines in Python and Spark processing 50M+ daily events for real-time analytics dashboards.
• Presented findings to product leadership, contributing to retention strategy changes projected to save $5M annually.

Data Science Research Intern — NASA Jet Propulsion Laboratory

2023-06 – 2023-09

• Applied computer vision techniques to satellite imagery analysis, improving land-cover classification accuracy by 15% using deep learning models.
• Built a time-series forecasting model for telemetry data anomaly detection, achieving 95% recall on critical system alerts.
• Co-authored a research paper on semi-supervised learning for remote sensing published in a peer-reviewed journal.

Education

• M.S. in Data Science, Stanford University (2023-09 – 2025-06)
• B.S. in Mathematics & Computer Science, University of Texas at Austin (2019-08 – 2023-05)

Certifications

• AWS Certified Data Analytics - Specialty (2024)
• DeepLearning.AI TensorFlow Developer (2024)

Publications

• Patel, J. et al. "Semi-Supervised Learning for Remote Sensing Classification" - IEEE IGARSS 2024

Projects

• Built an end-to-end ML pipeline for real-time sentiment analysis of social media data (10K+ daily predictions)
• Developed a recommender system using collaborative filtering achieving 15% improvement over baseline (Deployed on Streamlit, 500+ users)
• Created a Kaggle notebook series on time-series forecasting (featured, 50K+ views)

Leadership

• Data Science Club President, Stanford (2024-2025)
• Organized the Stanford Data Science Conference with 300+ attendees

Hackathons

• 1st Place, TreeHacks 2024 - AI-powered disaster response mapping tool
• Best Data Science Hack, CalHacks 2023